Association of Vascular Risk Factors and Cerebrovascular Pathology With Alzheimer Disease Pathologic Changes in Individuals Without Dementia.

BACKGROUND AND OBJECTIVES: Vascular risk factors (VRFs) and cerebral small vessel disease (cSVD) are common in patients with Alzheimer disease (AD). It remains unclear whether this coexistence reflects shared risk factors or a mechanistic relationship and whether vascular and amyloid pathologies have independent or synergistic influence on subsequent AD pathophysiology in preclinical stages. We investigated links between VRFs, cSVD, and amyloid levels (Aß1-42) and their combined effect on downstream AD biomarkers, that is, CSF hyperphosphorylated tau (P-tau181), atrophy, and cognition. METHODS: This retrospective study included nondemented participants (Clinical Dementia Rating < 1) from the European Prevention of Alzheimer's Dementia (EPAD) cohort and assessed VRFs with the Framingham risk score (FRS) and cSVD features on MRI using visual scales and white matter hyperintensity volumes. After preliminary linear analysis, we used structural equation modeling (SEM) to create a "cSVD severity" latent variable and assess the direct and indirect effects of FRS and cSVD severity on Aß1-42, P-tau181, gray matter volume (baseline and longitudinal), and cognitive performance (baseline and longitudinal). RESULTS: A total cohort of 1,592 participants were evaluated (mean age = 65.5 ± 7.4 years; 56.16% F). We observed positive associations between FRS and all cSVD features (all p < 0.05) and a negative association between FRS and Aß1-42 (ß = -0.04 ± 0.01). All cSVD features were negatively associated with CSF Aß1-42 (all p < 0.05). Using SEM, the cSVD severity fully mediated the association between FRS and CSF Aß1-42 (indirect effect: ß = -0.03 ± 0.01), also when omitting vascular amyloid-related markers. We observed a significant indirect effect of cSVD severity on P-tau181 (indirect effect: ß = 0.12 ± 0.03), baseline and longitudinal gray matter volume (indirect effect: ß = -0.10 ± 0.03; ß = -0.12 ± 0.05), and baseline cognitive performance (indirect effect: ß = -0.16 ± 0.03) through CSF Aß1-42. DISCUSSION: In a large nondemented population, our findings suggest that cSVD is a mediator of the relationship between VRFs and CSF Aß1-42 and affects downstream neurodegeneration and cognitive impairment. We provide evidence of VRFs indirectly affecting the pathogenesis of AD, highlighting the importance of considering cSVD burden in memory clinics for AD risk evaluation and as an early window for intervention. These results stress the role of VRFs and cerebrovascular pathology as key biomarkers for accurate design of anti-amyloid clinical trials and offer new perspectives for patient stratification.

Alzheimer's disease genetic pathways impact cerebrospinal fluid biomarkers and imaging endophenotypes in non-demented individuals.

INTRODUCTION: Unraveling how Alzheimer's disease (AD) genetic risk is related to neuropathological heterogeneity, and whether this occurs through specific biological pathways, is a key step toward precision medicine. METHODS: We computed pathway-specific genetic risk scores (GRSs) in non-demented individuals and investigated how AD risk variants predict cerebrospinal fluid (CSF) and imaging biomarkers reflecting AD pathology, cardiovascular, white matter integrity, and brain connectivity. RESULTS: CSF amyloidbeta and phosphorylated tau were related to most GRSs. Inflammatory pathways were associated with cerebrovascular disease, whereas quantitative measures of white matter lesion and microstructure integrity were predicted by clearance and migration pathways. Functional connectivity alterations were related to genetic variants involved in signal transduction and synaptic communication. DISCUSSION: This study reveals distinct genetic risk profiles in association with specific pathophysiological aspects in predementia stages of AD, unraveling the biological substrates of the heterogeneity of AD-associated endophenotypes and promoting a step forward in disease understanding and development of personalized therapies. HIGHLIGHTS: Polygenic risk for Alzheimer's disease encompasses six biological pathways that can be quantified with pathway-specific genetic risk scores, and differentially relate to cerebrospinal fluid and imaging biomarkers. Inflammatory pathways are mostly related to cerebrovascular burden. White matter health is associated with pathways of clearance and membrane integrity, whereas functional connectivity measures are related to signal transduction and synaptic communication pathways.

Alzheimer's Disease and Small Vessel Disease Differentially Affect White Matter Microstructure.

OBJECTIVE: Alzheimer's disease (AD) and cerebral small vessel disease (cSVD), the two most common causes of dementia, are characterized by white matter (WM) alterations diverging from the physiological changes occurring in healthy aging. Diffusion tensor imaging (DTI) is a valuable tool to quantify WM integrity non-invasively and identify the determinants of such alterations. Here, we investigated main effects and interactions of AD pathology, APOE-ε4, cSVD, and cardiovascular risk on spatial patterns of WM alterations in non-demented older adults. METHODS: Within the prospective European Prevention of Alzheimer's Dementia study, we selected 606 participants (64.9 ± 7.2 years, 376 females) with baseline cerebrospinal fluid samples of amyloid ß1-42 and p-Tau181 and MRI scans, including DTI scans. Longitudinal scans (mean follow-up time = 1.3 ± 0.5 years) were obtained in a subset (n = 223). WM integrity was assessed by extracting fractional anisotropy and mean diffusivity in relevant tracts. To identify the determinants of WM disruption, we performed a multimodel inference to identify the best linear mixed-effects model for each tract. RESULTS: AD pathology, APOE-ε4, cSVD burden, and cardiovascular risk were all associated with WM integrity within several tracts. While limbic tracts were mainly impacted by AD pathology and APOE-ε4, commissural, associative, and projection tract integrity was more related to cSVD burden and cardiovascular risk. AD pathology and cSVD did not show any significant interaction effect. INTERPRETATION: Our results suggest that AD pathology and cSVD exert independent and spatially different effects on WM microstructure, supporting the role of DTI in disease monitoring and suggesting independent targets for preventive medicine approaches.

Comparative analysis of multimodal biomarkers for amyloid-beta positivity detection in Alzheimer's disease cohorts.

Introduction: Efforts to develop cost-effective approaches for detecting amyloid pathology in Alzheimer's disease (AD) have gained significant momentum with a focus on biomarker classification. Recent research has explored non-invasive and readily accessible biomarkers, including magnetic resonance imaging (MRI) biomarkers and some AD risk factors. Methods: In this comprehensive study, we leveraged a diverse dataset, encompassing participants with varying cognitive statuses from multiple sources, including cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and our in-house Dementia Disease Initiation (DDI) cohort. As brain amyloid plaques have been proposed as sufficient for AD diagnosis, our primary aim was to assess the effectiveness of multimodal biomarkers in identifying amyloid plaques, using deep machine learning methodologies. Results: Our findings underscore the robustness of the utilized methods in detecting amyloid beta positivity across multiple cohorts. Additionally, we investigated the potential of demographic data to enhance MRI-based amyloid detection. Notably, the inclusion of demographic risk factors significantly improved our models' ability to detect amyloid-beta positivity, particularly in early-stage cases, exemplified by an average area under the ROC curve of 0.836 in the unimpaired DDI cohort. Discussion: These promising, non-invasive, and cost-effective predictors of MRI biomarkers and demographic variables hold the potential for further refinement through considerations like APOE genotype and plasma markers.

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021.

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3-Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.

Genetic, vascular and amyloid components of cerebral blood flow in a preclinical population.

Aging-related cognitive decline can be accelerated by a combination of genetic factors, cardiovascular and cerebrovascular dysfunction, and amyloid-ß burden. Whereas cerebral blood flow (CBF) has been studied as a potential early biomarker of cognitive decline, its normal variability in healthy elderly is less known. In this study, we investigated the contribution of genetic, vascular, and amyloid-ß components of CBF in a cognitively unimpaired (CU) population of monozygotic older twins. We included 134 participants who underwent arterial spin labeling (ASL) MRI and [18F]flutemetamol amyloid-PET imaging at baseline and after a four-year follow-up. Generalized estimating equations were used to investigate the associations of amyloid burden and white matter hyperintensities with CBF. We showed that, in CU individuals, CBF: 1) has a genetic component, as within-pair similarities in CBF values were moderate and significant (ICC > 0.40); 2) is negatively associated with cerebrovascular damage; and 3) is positively associated with the interaction between cardiovascular risk scores and early amyloid-ß burden, which may reflect a vascular compensatory response of CBF to early amyloid-ß accumulation. These findings encourage future studies to account for multiple interactions with CBF in disease trajectory analyses.

Eigenvector centrality dynamics are related to Alzheimer's disease pathological changes in non-demented individuals.

Amyloid-ß accumulation starts in highly connected brain regions and is associated with functional connectivity alterations in the early stages of Alzheimer's disease. This regional vulnerability is related to the high neuronal activity and strong fluctuations typical of these regions. Recently, dynamic functional connectivity was introduced to investigate changes in functional network organization over time. High dynamic functional connectivity variations indicate increased regional flexibility to participate in multiple subnetworks, promoting functional integration. Currently, only a limited number of studies have explored the temporal dynamics of functional connectivity in the pre-dementia stages of Alzheimer's disease. We study the associations between abnormal cerebrospinal fluid amyloid and both static and dynamic properties of functional hubs, using eigenvector centrality, and their relationship with cognitive performance, in 701 non-demented participants from the European Prevention of Alzheimer's Dementia cohort. Voxel-wise eigenvector centrality was computed for the whole functional magnetic resonance imaging time series (static), and within a sliding window (dynamic). Differences in static eigenvector centrality between amyloid positive (A+) and negative (A-) participants and amyloid-tau groups were found in a general linear model. Dynamic eigenvector centrality standard deviation and range were compared between groups within clusters of significant static eigenvector centrality differences, and within 10 canonical resting-state networks. The effect of the interaction between amyloid status and cognitive performance on dynamic eigenvector centrality variability was also evaluated with linear models. Models were corrected for age, sex, and education level. Lower static centrality was found in A+ participants in posterior brain areas including a parietal and an occipital cluster; higher static centrality was found in a medio-frontal cluster. Lower eigenvector centrality variability (standard deviation) occurred in A+ participants in the frontal cluster. The default mode network and the dorsal visual networks of A+ participants had lower dynamic eigenvector centrality variability. Centrality variability in the default mode network and dorsal visual networks were associated with cognitive performance in the A- and A+ groups, with lower variability being observed in A+ participants with good cognitive scores. Our results support the role and timing of eigenvector centrality alterations in very early stages of Alzheimer's disease and show that centrality variability over time adds relevant information on the dynamic patterns that cause static eigenvector centrality alterations. We propose that dynamic eigenvector centrality is an early biomarker of the interplay between early Alzheimer's disease pathology and cognitive decline.

Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer's disease and neurodegeneration stratified by sex.

Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer's disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD, and OASIS. Brain-age delta was associated with abnormal amyloid-ß, more advanced stages (AT) of AD pathology and APOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging in non-demented individuals with abnormal levels of biomarkers of AD and axonal injury.

In-Hospital Predictors of Need for Ventilatory Support and Mortality in Chest Trauma: A Multicenter Retrospective Study.

Chest trauma management often requires the use of invasive and non-invasive ventilation. To date, only a few studies investigated the predictors of the need for ventilatory support. Data on 1080 patients with chest trauma managed in two different centers were retrospectively analyzed. Univariate and multivariate analyses were performed to identify the predictors of tracheal intubation (TI), non-invasive mechanical ventilation (NIMV), and mortality. Rib fractures (p = 0.0001) fracture of the scapula, clavicle, or sternum (p = 0.045), hemothorax (p = 0.0035) pulmonary contusion (p = 0.0241), and a high Injury Severity Score (ISS) (p ≤ 0001) emerged as independent predictors of the need of TI. Rib fractures (p = 0.0009) hemothorax (p = 0.0027), pulmonary contusion (p = 0.0160) and a high ISS (p = 0.0001) were independent predictors of NIMV. The center of trauma care (p = 0.0279), age (p < 0.0001) peripheral oxygen saturation in the emergency department (p = 0.0010), ISS (p < 0.0001), and Revised Trauma Score (RTS) (p < 0.0001) were independent predictors of outcome. In conclusion, patients who do not require TI, while mandating ventilatory support with selected types of injuries and severity scores, are more likely to be subjected to NIMV. Trauma team expertise and the level of the trauma center could influence patient outcomes.

Current state and guidance on arterial spin labeling perfusion MRI in clinical neuroimaging.

This article focuses on clinical applications of arterial spin labeling (ASL) and is part of a wider effort from the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group to update and expand on the recommendations provided in the 2015 ASL consensus paper. Although the 2015 consensus paper provided general guidelines for clinical applications of ASL MRI, there was a lack of guidance on disease-specific parameters. Since that time, the clinical availability and clinical demand for ASL MRI has increased. This position paper provides guidance on using ASL in specific clinical scenarios, including acute ischemic stroke and steno-occlusive disease, arteriovenous malformations and fistulas, brain tumors, neurodegenerative disease, seizures/epilepsy, and pediatric neuroradiology applications, focusing on disease-specific considerations for sequence optimization and interpretation. We present several neuroradiological applications in which ASL provides unique information essential for making the diagnosis. This guidance is intended for anyone interested in using ASL in a routine clinical setting (i.e., on a single-subject basis rather than in cohort studies) building on the previous ASL consensus review.